Formulate effective economic and business decisions

Assignment Help Econometrics

Reference no: EM133886367

Overview
This is an individual assignment. You are required to download the assignment rile, carry out regressions in R and analyse the regression results. Alter answering the questions, please upload your file to Canvas, including your R code.

Learning Outcome 1: Critically analyse and synthesise data to formulate effective economic and business decisions.

Learning Outcome 2: Effectively communicate business data and econometric concepts to professionals and nonprofessionals in diverse business contexts.

Learning Outcome 3: Apply technical and analytical knowledge to provide specialist advice to people from business disciplines in diverse business contexts.

Learning Outcome 4: Apply reasoned judgements to solve a variety of statistical and econometric problems in business disciplines such as Accounting, Economics, Finance and Marketing, with reference to business, government policy and global perspectives.

Learning Outcome 5: Use the feedback given on your performance to identify action strategies that required for improvement.

Use the dataset: LifeExpectancy.RData

Estimating the driving factors of life expectancy internationally provides valuable insights into the factors influencing the health and longevity of populations across different countries. It enables researchers to identify socio-economic, environmental, and healthcare-related variables that contribute to disparities in life expectancy, helping governments and organizations design targeted policies to improve public health. Moreover, understanding these determinants aids in forecasting future trends, assessing the effectiveness of current health interventions, and promoting equitable access to healthcare resources. By analysing these factors through econometric models, we can develop a comprehensive understanding of the complex interplay between economics and health, ultimately contributing to global well-being and development strategies. Get professional assignment help service now!

You are a newly hired analyst tasked to model national life expectancy worldwide. Assume that the outgoing research officer had started working on the econometric model to assess some of the drivers of life expectancy. Now as an incoming research officer your job is to finish this research. Your variables of interest, which originate form the World Development Indicators (World Bank), for the year 2023 are:

Life_exp = Life expectancy at birth, total (years) [SP.DYN.LE00.IN]
GPPpc = GDP per capita, PPP (constant 2021 international $) [NY.GDP.PCAP.PP.KD]
UnderNourished = Prevalence of undernourishment (% of population) [SN.ITK.DEFC.ZS]
DrinkingWater = People using at least basic drinking water services (% of population) [SH.H2O.BASW.ZS]
TB= Incidence of tuberculosis (per 100,000 people) [SH.TBS.INCD]
Immunization = Immunization, DPT (% of children ages 12-23 months) [SH.IMM.IDPT]

Dependent variable:
Life expectancy at birth, total (years): We would like to estimate the relationship of other factors with this variable. It is defined as the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.

Explanatory variables:
GDP per capita (GDPpc): The richer a country is, some scholars expect higher life expectancy, due to several factors, among others higher hygiene, better access to services etc. This variable is likely to be highly multicollinear with other explanatory variables.
Prevalence of undernourishment (% of population): Undernourishment is expected to be in a negative relationship with life expectancy.
People using at least basic drinking water services (% of population): It is expected that as higher percentage of the population has access to basic drinking water services, life expectancy will increase at an accelerating rate, because the initial benefits from reduced waterborne diseases are amplified by improvements in related factors such as sanitation and healthcare.

Incidence of tuberculosis (per 100,000 people): Higher incidence of tuberculosis directly reduces life expectancy due to increased mortality from the disease and reflects underlying health vulnerabilities that shorten lives.

Immunization, DPT (% of children ages 12-23 months): Immunisation at early ages against diphtheria, pertussis, and tetanus, is a key scientific achievement aimed at reducing childhood mortality. We expect a positive relationship with life expectancy and a high correlation with GDPpc.
All data originate from the World Bank (WDI). Please assess whether the above variables are truly associated with life expectancy, and if yes, how. Answer the following questions:

QUESTIONS:

Use R to run the following cross-sectional regression. (Please note the natural logs and construct these in R as needed):

Life_exp = β₀ + β₁log(GDPpc) + β₂UnderNourished + β₃DrinkingWater + β₄log(TB) + β₅Immunization + u (Equation 1)

Present your regression results in a table below (R output):

Interpret the constant (2.5 marks) and its p-value.

Interpret the coefficient on GDP per capita and its p-value.

Interpret the coefficient on the % of people using at least basic drinking water services and its p- value

Interpret the coefficient on Incidence of tuberculosis (per 100,000 people) and its p-value

Interpret the coefficient Immunization, DPT (% of children ages 12-23 months) and calculate its t-stat. Interpret the calculated t-statistic

Interpret the R2 of the regression.

Several explanatory variables would be in a multicollinear relationship with each other. Explain perfect and imperfect multicollinearity and present a correlation matrix between the independent variables in Equation 1.

Describe each of the Gauss-Markov assumptions and specify if they are likely to hold for the regression in Question 1 or not.

Run the following regression with a quadratic drinking water term added to the original regression:

Life Expectancy = β₀ + β₁log(GDPpc) + β₂UnderNourished + β₃DrinkingWater + β₄DrinkingWater² + β₄log(TB) + β₅Immunization + u (Equation 2)

Present your regression results in a table below (R output):

Is the relationship U-shaped or inverted U shaped? Is this a significant relationship?

Calculate the turning point of the quadratic relationship, and please analyse the result.

Present a functioning R code reproducing the results below (not in a separate file). This is a critical part of the assignment without which we'll initiate a plagiarism check.

Reference no: EM133886367

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